A MARKOVIAN LOCAL RESAMPLING SCHEME FOR NONPARAMETRIC ESTIMATORS IN TIME SERIES ANALYSIS
研究了一种p阶马尔可夫局部重抽样方法，用于近似条件期望的非参数核估计量的分布，在比平稳马尔可夫过程更广的随机过程类中证明了渐近有效性，并通过模拟展示了有限样本性能。
In this paper we study the properties of a pth-order Markovian local resampling procedure in approximating the distribution of nonparametric (kernel) estimators of the conditional expectation m(x;φ). Under certain regularity conditions, asymptotic validity of the proposed resampling scheme is established for a class of stochastic processes that is broader than the class of stationary Markov processes. Some simulations illustrate the finite sample performance of the proposed resampling procedure.